Optimizing subsurface carbon-energy synergy by balancing diffusion and convection via physics-informed Bayesian learning

Optimizing subsurface carbon-energy synergy by balancing diffusion and convection via physics-informed Bayesian learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Optimizing subsurface carbon-energy synergy by balancing diffusion and convection via physics-informed Bayesian learning Zongfa Li, Guihua Yang, Maoheng Li, Yuhui Zhou, Jingwei Huang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9073871/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The co-optimization of enhanced oil recovery and carbon dioxide sequestration in shale reservoirs is fundamentally constrained by the competing physics of molecular diffusion and pressure-driven convection—a challenge that existing data-driven optimization frameworks fail to address due to their black-box nature and lack of physical fidelity. Here, this study introduce a physics-informed adaptive ensemble surrogate-assisted Bayesian optimization framework (AES-BO) that synergistically integrates Gaussian process regression, polynomial response surface, and radial basis function networks. By dynamically weighting these surrogates based on real-time cross-validation error, AES-BO embeds physical priors into the optimization loop, enabling it to navigate the complex, non-convex parameter space of CO₂-N₂ hybrid huff-n-puff while respecting the underlying diffusion-convection trade-off. Applied to a field-scale shale oil model, our framework achieved a global optimum net present value of 64.2 million—outperforming state-of-the-art differential evolution–artificial neural network, differential evolution–support vector regression and particle swarm optimization methods by 2.2–2.8%—while accelerating computation by up to 82.7%. Global sensitivity analysis revealed that the economic outcome is dominantly controlled by injection rate and soaking time, but is critically governed by a strong non-linear coupling between cyclic injection volume and the multi-well production regime. The optimized strategy enhances the recovery factor by 8.23% by using CO₂ for nano-scale oil mobilization and N₂ for macro-scale pressure maintenance, and identifies a clear economic limit of three huff-n-puff cycles. This work establishes a generalizable, physics-guided paradigm for the intelligent design of low-carbon subsurface energy systems, directly linking operational decisions to fundamental transport physics. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics Geological carbon storage Nanoconfined multiphase flow Diffusion-convection coupling Physics-informed machine learning Bayesian active learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 22 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers invited by journal 19 Mar, 2026 Editor invited by journal 16 Mar, 2026 Editor assigned by journal 11 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 09 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9073871","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":610517249,"identity":"abbde3ce-5319-4c4d-9998-8025b689cb50","order_by":0,"name":"Zongfa 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Here, this study introduce a physics-informed adaptive ensemble surrogate-assisted Bayesian optimization framework (AES-BO) that synergistically integrates Gaussian process regression, polynomial response surface, and radial basis function networks. By dynamically weighting these surrogates based on real-time cross-validation error, AES-BO embeds physical priors into the optimization loop, enabling it to navigate the complex, non-convex parameter space of CO₂-N₂ hybrid huff-n-puff while respecting the underlying diffusion-convection trade-off. Applied to a field-scale shale oil model, our framework achieved a global optimum net present value of 64.2 million\u0026mdash;outperforming state-of-the-art differential evolution\u0026ndash;artificial neural network, differential evolution\u0026ndash;support vector regression and particle swarm optimization methods by 2.2\u0026ndash;2.8%\u0026mdash;while accelerating computation by up to 82.7%. 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This work establishes a generalizable, physics-guided paradigm for the intelligent design of low-carbon subsurface energy systems, directly linking operational decisions to fundamental transport physics.\u003c/p\u003e","manuscriptTitle":"Optimizing subsurface carbon-energy synergy by balancing diffusion and convection via physics-informed Bayesian learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 11:00:47","doi":"10.21203/rs.3.rs-9073871/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-23T08:36:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62537056050622113330929372331852346811","date":"2026-03-22T21:42:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36318139354123963537778165838862193037","date":"2026-03-20T00:34:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T15:56:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-16T20:46:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-11T10:32:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-11T10:31:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-09T13:45:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4d33c5df-123b-40d0-a9ad-915ae1bce997","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64953354,"name":"Physical sciences/Energy science and technology"},{"id":64953355,"name":"Physical sciences/Engineering"},{"id":64953356,"name":"Physical sciences/Mathematics and computing"},{"id":64953357,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2026-05-13T01:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 11:00:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9073871","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9073871","identity":"rs-9073871","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}